Academic Recommendation System Based on the Similarity Learning of the Citation Network Using Citation Impact

Abstract

In today's significant and rapidly increasing amount of scientific publications, exploring recent studies in a given research area and building an effective scientific collaboration has become more challenging than any time before. Scientific production growth has been increasing the difficulties for identifying the most relevant papers to cite or to find an appropriate conference or journal to submit a paper to publish. As a result, authors and publishers rely on different analytical approaches in order to measure the relationship among the citation network. Different parameters have been used such as the impact factor, number of citations, co-citation to assess the impact of the produced research publication. However, using one assessing factor considers only one level of relationship exploration, since it does not reflect the effect of the other factors. In this thesis, we propose an approach to measure the Academic Citation Impact that will help to identify the impact of articles, authors, and venues at their extended nearby citation network. We combine the content similarity with the bibliometric indices to evaluate the citation impact of articles, authors, and venues in their surrounding citation network. Using the article metadata, we calculate the semantic similarity between any two articles in the extended network. Then we use the similarity score and bibliometric indices to evaluate the impact of the articles, authors, and venues among their extended nearby citation network. Furthermore, we propose an academic recommendation model to identify the latent preferences among the citation network of the given article in order to expose the concealed connection between the academic objects (articles, authors, and venues) at the citation network of the given article. To reveal the degree of trust for collaboration between academic objects (articles, authors, and venues), we use the similarity learning to estimate the collaborative confidence score that represents the anticipation of a prospect relationship between the academic objects among a scientific community. We conducted an offline experiment to measure the accuracy of delivering personalized recommendations, based on the user’s selection preferences; real-world datasets were used. Our evaluation results show a potential improvement to the quality of the recommendation when compared to baseline recommendation algorithms that consider co-citation information.